Currently, various sensor platforms can be used for persistently monitoring very large areas. For example, Wide Area Motion Imagery (WAMI) systems mounting on aerial platform flying at around 7,000 feet can be used as an aid in disaster relief, as well as traffic and accident management. Such systems typically produce an overwhelmingly large amount of information. Monitoring such a large amount of data with a human operator is not feasible, which calls for an automated method of processing the collected imagery.
Traditional visual detection algorithms mainly focus on detecting a limited number of objects in small scenes and therefore cannot be directly generalized to WAMI scenarios.
The large scale images taken by WAMI systems are more than 8,000,000 pixels in resolution. Objects in WAMI data are much smaller than that collected from imagery around 2000 feet, with vehicle sizes ranging from 4 pixels to 70 pixels in grayscale image groups. The lack of computationally efficient imagery analysis tools has become a bottleneck for utilizing WAMI data for urban surveillance.
Accordingly, it is desirable to provide methods and systems for detecting multiple moving objects based on large scale aerial images via high performance computation technology.